The Self-and-Self Model: A General Computational Framework for Adaptive Selection and Coordination Among Internal Computational Mechanisms
Abstract
Computational systems frequently contain multiple mechanisms for processing information, generating solutions, evaluating results, correcting errors, and allocating computational resources. These mechanisms may operate independently, perform complementary functions, or depend on the outputs of other mechanisms within the same system. However, systems are often designed around predefined computational procedures, fixed processing sequences, or manually specified rules for selecting among available mechanisms. Determining which internal mechanism should operate, when it should be activated, and when its contribution is sufficient remains an important problem in adaptive computation.
This paper proposes the Self-and-Self Model (SSM), a general computational framework for adaptively selecting, switching between, and coordinating distinct computational mechanisms within a single computational system. Unlike frameworks centered on interaction between separate components, SSM focuses on the internal organizational dimension of computation: how a system allocates its own computational activity among alternative mechanisms according to task requirements, internal state, uncertainty, expected computational benefit, resource constraints, and observed outcomes.
The framework represents a system through a set of internal computational mechanisms, a mechanism-dependency structure, a state-dependent selection policy, a computational budget, and a feedback-driven adaptation process. Its central principle is that a system should not necessarily employ every available mechanism or follow one fixed internal procedure. Instead, it should determine which mechanism or combination of mechanisms is appropriate for the current computational state. The paper formalizes mechanism selection, switching costs, activation dependencies, stopping decisions, and adaptive policy updates. It also presents a reference algorithm, conditional theoretical propositions, complexity considerations, proposed experimental hypotheses, and a cross-domain evaluation program.
SSM is intended as an organizational abstraction that can be instantiated in artificial intelligence, numerical optimization, scientific computing, planning, diagnosis, control, and other systems containing multiple internal computational procedures. Its contribution must be established through formal analysis and empirical comparison with existing methods for metareasoning, algorithm selection, adaptive computation, and computational resource allocation.
Related
- The Self-and-Mutual-and-Meta Model: A General Computational Framework for Adaptive Coordination, Selection, and Regulation of Autonomous and Interactive Mechanisms
- The Self-and-Mutual-and-World Model: A General Computational Framework for Adaptive Coordination Between Autonomous Computation, Interactive Mechanisms, and World-State Modeling
- The Self-and-Self-and-World Model: A General Computational Framework for Adaptive Coordination Between Internal Computational Mechanisms and World-State Modeling
- The Self-and-Mutual-and-Causality Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Causal Reasoning
- The Self-and-Mutual-and-Action Model: A General Computational Framework for Adaptive Coordination Between Individual Processing, Interactive Mechanisms, and Goal-Directed Action
BibTeX
@misc{zhang2026selfandself,
title = {{The Self-and-Self Model: A General Computational Framework for Adaptive Selection and Coordination Among Internal Computational Mechanisms}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23432411},
url = {https://zenodo.org/records/23432411},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Self-and-Self Model: A General Computational Framework for Adaptive Selection and Coordination Among Internal Computational Mechanisms
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/17
PB - Zenodo
DO - 10.5281/zenodo.23432411
UR - https://zenodo.org/records/23432411
AB - Computational systems frequently contain multiple mechanisms for processing information, generating solutions, evaluating results, correcting errors, and allocating computational resources. These mechanisms may operate independently, perform complementary functions, or depend on the outputs of other mechanisms within the same system. However, systems are often designed around predefined computational procedures, fixed processing sequences, or manually specified rules for selecting among available mechanisms. Determining which internal mechanism should operate, when it should be activated, and when its contribution is sufficient remains an important problem in adaptive computation. This paper proposes the Self-and-Self Model (SSM), a general computational framework for adaptively selecting, switching between, and coordinating distinct computational mechanisms within a single computational system. Unlike frameworks centered on interaction between separate components, SSM focuses on the internal organizational dimension of computation: how a system allocates its own computational activity among alternative mechanisms according to task requirements, internal state, uncertainty, expected computational benefit, resource constraints, and observed outcomes. The framework represents a system through a set of internal computational mechanisms, a mechanism-dependency structure, a state-dependent selection policy, a computational budget, and a feedback-driven adaptation process. Its central principle is that a system should not necessarily employ every available mechanism or follow one fixed internal procedure. Instead, it should determine which mechanism or combination of mechanisms is appropriate for the current computational state. The paper formalizes mechanism selection, switching costs, activation dependencies, stopping decisions, and adaptive policy updates. It also presents a reference algorithm, conditional theoretical propositions, complexity considerations, proposed experimental hypotheses, and a cross-domain evaluation program. SSM is intended as an organizational abstraction that can be instantiated in artificial intelligence, numerical optimization, scientific computing, planning, diagnosis, control, and other systems containing multiple internal computational procedures. Its contribution must be established through formal analysis and empirical comparison with existing methods for metareasoning, algorithm selection, adaptive computation, and computational resource allocation.
ER -